tlbo-based routing approach for wireless sensor...

Received: August 22, 2017 94

International Journal of Intelligent Engineering and Systems, Vol.11, No.1, 2018 DOI: 10.22266/ijies2018.0228.10

TLBO-Based Routing Approach for Wireless Sensor Networks

Asmae El Ghazi1* Belaîd Ahiod1 Mohammed Abbad1

1LRIT, Associated Unit to CNRST (URAC 29) Faculty of Sciences,

Mohammed V University in Rabat, Morocco

* Corresponding author’s Email: [email protected]

Abstract: Routing in Wireless Sensor Networks (WSN) differs from routing in classical networks, as in WSN links

are unreliable. Indeed, there is no infrastructure and the sensors have limited energy resources and low processing

capacity. Routing problems in many cases are considered NP-hard problems. In these cases, metaheuristics can be

the best solution for this kind of problems. Thus, many metaheuristics are used for routing in WSNs. Ant colony

optimization (ACO) is among the most used metaheuristics, as it showed an excellent performance for routing. Due

to the adaptation difficulties related to the ACO parameters, in this paper we propose a new optimization approach

for routing problem using the Teaching Learning Based Optimization (TLBO). TLBO is a recent nature inspired

metaheuristic based on the influence of the teacher in the class and learners interactions. In this work, TLBO

algorithm is used for routing as it was proposed for continuous optimization problems, despite the fact that the

routing in WSNs is a discrete optimization problem. Our proposed approach is shown to be competitive with ACO-

based approaches in terms of energy consumption and WSN lifetime. The experiments performed in varied scenarios

and the results found, illustrate the efficiency of the TLBO approach against the well-known ACO approaches:

Energy Efficient Ant Based Routing (EEABR) Algorithm and the Improved Ant Colony Optimization Routing

Protocol (IACO).

Keywords: Wireless sensor network, Metaheuristic, Routing, Ant colony optimization, Teaching-learning-based

optimization.

1. Introduction

Wireless Sensor Networks represent an

attractive research area due to several factors such as

the resource-constrained nature of sensor nodes,

interference, energy consumption and a large

number of both commercial and military

applications that this technology offers [1].

Successful network design and deployment include

understanding and modelling several problems

related to these factors, which determine the

accessible extent and data rate of a WSN, as well as

cost and battery lifetime [2]. Therefore, studies,

intended to researchers and graduate fields related to

operations research, applied mathematics and

computer science, give some highlights on a number

of representative network problems in WSN and

focus on their respective optimization problems [3].

NP-hard optimization problems induce the necessity

of optimization methods like metaheuristics. In

WSN, the most studied optimization problem is the

routing problem, as it is a major challenge.

To resolve routing in wireless sensor networks a

diverse range of metaheuristic algorithms are used

including Artificial Bee Colony (ABC) [4], Genetic

Algorithms (GA) [5], Particle Swarm Optimization

(PSO) [6], Harmony Search (HS) [7] and Ant

Colony Optimization (ACO) [8], etc.

The ACO metaheuristic has been successfully

applied to solve routing problems in WSN [9, 10].

Some examples of ant based application include:

Sensor-driven Cost-aware Ant Routing (SC), the

Flooded Forward Ant Routing (FF) algorithm [11],

the Flooded Piggy-backed Ant Routing (FP)

algorithm, the Adaptive ant-based Dynamic Routing

(ADR) [12], the Adaptive Routing (AR) Improved

Adaptive Routing (IAR) algorithm [13], E&D



ANtraTS [14], Energy Efficient Ant Based Routing

(EEABR) Algorithm [15] and the Improved Ant

Colony Optimization Routing Protocol (IACO) [8].

A new efficient metaheuristic has recently been

developed: Teaching Learning Based Optimization

(TLBO) [16, 17]. TLBO algorithm is based on

teacher influence on learners’ output in a class. This

metaheuristic is characterized by the reduced

number of parameters, as the complexity of the

known metaheuristics is the number of parameters

used. In this paper, we propose a new optimization

approach for the routing problems using TLBO as it

was proposed for a continuous optimization

problems. To demonstrate the efficiency of the

proposed TLBO protocol, many simulations have

been performed in MATLAB, using various ranges

of nodes, a different detection zone area, a random

source nodes and a random deployment. The

obtained results demonstrate that our based model

gives a good performance in terms of energy

consumption, data delivery and reliability.

The remaining of the paper is organized as

follows: Section 2 presents the routing approach

based on the ACO. Section 3 describes the main

steps of our routing approach based on the TLBO

and an illustrative example is given in the same

section. Section 4 shows the performance evaluation

of our algorithm. Finally, Section 5 concludes our

work.

2. Routing approach based on the ACO

The authors in [8] have proposed a routing

approach based on ant colony optimization (IACO),

which has provided good results compared with

others ant colony optimization approaches. The ant

approach proposed is based on the following

algorithm of Ant colony optimization, used to find a

good path from the source node to the sink.

2.1 Ant colony optimization algorithm

The ant colony has a high ability to explore and

exploit their environment and using it as a medium

to share information. M. Dorigo and G. Di Caro

have been inspired by ant colonies in nature in order

to create ant colony optimization algorithms [18].

The ACO algorithm basic steps are presented in

Algorithm 1.

An ant moves from a node 𝑠 to a node 𝑟 with the

highest probability value. Every ant walking

between nodes lets an amount of pheromone 𝛿𝜏𝑖𝑗 . This quantity of pheromone changes following

environmental factors (wind, sun...), which are

presented by the evaporation rate parameter 𝜌.

2.2 Routing using ant colony optimization

2.2.1. Improved ant colony optimization

The ACO approach is proposed for routing in

flat networks with static sensors and sink, in order to

minimize the energy consumption. When an event is

detected the information is transmitted to the sink

separated on 𝑁 parts. The ants use the probabilistic

decision rules Eq. (1) in the path from source to the

base station.

𝑃𝑘(𝑟, 𝑠) =

{[𝜏(𝑟,𝑠)]𝛼.[𝜂(𝑟,𝑠)]𝛽.[𝛿(𝑟,𝑠)]𝛾

∑ [𝜏(𝑟,𝑠)]𝛼.[𝜂(𝑟,𝑠)]𝛽.[𝛿(𝑟,𝑠)]𝛾𝑟𝜖𝑅𝑠

𝑖𝑓 𝑘 ∉ 𝑡𝑎𝑏𝑢𝑟

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (1)

Where 𝜏 (𝑟, 𝑠) is the pheromone value between node

𝑠 and 𝑟, 𝜂 is the first heuristic value related to the

energy of nodes. 𝑅𝑠 are the receiver nodes, the

second heuristic value is 𝛿 used to distinguish the

best neighbor. 𝛼, 𝛽 and 𝛾 are the control parameters

relative to the pheromone influence and the

heuristics information. 𝑡𝑎𝑏𝑢𝑟 is the list of packet

identities already received by node 𝑟. This approach

gives good results, compared to Okdem et al.

approach [9]. However, the novel optimization

technique based on TLBO proposed in this paper

promises better results as demonstrated in the

following sections.

2.2.2. Energy-efficient ant-based routing

Camilo et al. proposed an Energy Efficient Ant

Based Routing (EEABR) Algorithm, which is based

ACO, to improve the energy efficiency of WSNs



and maximize the network lifetime [15]. This

routing algorithm, found the paths between the

source nodes and the sink by forward ants, which

select next hop according to the residual energy of

neighbors and the amount of pheromone left by

backward ant during its journey. Meanwhile, the

number of visited nodes, is introduced in this phase,

which makes those nodes closer to sink node have

more pheromone trail, so that forward ants could

reach the sink node more efficiently. However,

during the early period of transmitting packets, the

forward ants cannot find the optimal or near optimal

paths due to the little difference of the amount of

pheromone trail stored in routing table.

The algorithm uses a good strategy considering

energy levels of the nodes and the lengths of the

routed paths, which have a great contribution to

balancing the energy consumption of nodes and

reducing the time delay. Considering this fact, we

have compared the performance results of our

TLBO approach to the results of the EEABR

algorithm.

3. Our routing approach based on the

TLBO

TLBO approach is a new optimization technique

for routing problem in WSN, proposed to overcome

ant- approach drawbacks such as the parameters

tuning difficulties and the processing time,

considering the sensor limitations. The routing

process begins by the initialization of the population

of paths, then finding the optimum path using TLBO

algorithm described in the next section, then sends

data parts through it.

3.1 The TLBO algorithm

Inspired by the teacher’s influence in the class

and learners interaction, Rao et al. have developed

in 2012 the Teaching learning based optimization

algorithm [17]. This method has outperformed some

of the commonly used metaheuristics regarding

continuous nonlinear numerical optimization

problems. It could be split into two basic parts:

Teacher phase and Learner phase. TLBO has been

used successfully for many multi-objective

problems [19, 20]. Algorithm 2 describes TLBO

process.

TLBO algorithm starts by the initialization of

the population and the termination criterion, and

then researches the best solution using the teacher

and the learner phases. At the end, it verifies if the

termination criterion is satisfied or not.

Algorithm 2 TLBO

begin g ← 0;

initialize_population(P, pop_size)

evaluate(P)

repeat

for i = 1 → pop_size do

{Teacher Phase}

TF = round(1 + r)

Xmean ← calculate_mean_vector(P)

Xteacher ← best_solution(P)

Xnew = Xi + r · (Xteacher − (TF · Xmean))

evaluate(Xnew)

if Xnew better than Xi then

Xi ← Xnew

end if {End of Teacher Phase}

{Learner Phase}

ii ← random(pop_size){ii ≠ i}

if Xi better than Xii then

Xi,new = Xi + r · (Xi − Xii)

else

Xi,new = Xi + r · (Xii − Xi)

end if

evaluate(Xi,new)

if Xi,new better than Xi then

Xi ← Xi,new

end if {End of Learner Phase}

end for

P← remove_duplicate_individuals(P)

g ← g + 1

until (g ≠ num_gen) {termination_condition}

print_best_result(P)

end

3.2 Adapting TLBO for routing in WSN

We implement TLBO for routing problem

following five steps. In the first step, we initialize

the optimization parameters; in the second one we

generate the initial population. The third step

represents the teacher phase and the fourth one

presents the learner phase. Finally, in the last step

after verification, we stop the process or we restart.

Step 1: Initialization of the optimization

parameters considered for routing problems and

definition of the objective function.

The node detecting an event collects information

about available routes that lead to the base station.

By requesting neighbors for a definite time, a

number of paths form the initial population:

𝑃𝑎𝑡ℎ𝑠 = {𝑝1, . . . , 𝑝𝑖 , . . . , 𝑝𝑚}, where every path 𝑝𝑖 is formed a 𝑝𝑖 = {𝑛𝑠, … , 𝑛𝑘, … , 𝑛𝑠𝑖𝑛𝑘}.



Population size (𝑃𝑛): depends on the waiting time

that the source node fixed before requesting

neighbors.

Number of generations (𝐺𝑛): varied depending

on population size (if the population size is small the

algorithm converges faster).

Number of design variables (𝐷𝑛) = 3

Optimization problem: Minimize

𝑓(𝑝𝑖) =∑ 𝐸(𝑝𝑖).∑ 𝑑(𝑝𝑖)

𝑛1

𝑛1

𝑛 (2)

Where 𝑝𝑖 is the corresponding 𝑖𝑡ℎ element in 𝑃𝑛, 𝑛

is length of 𝑝𝑖 , 𝑑 (𝑝𝑖) is approximate delay for 𝑝𝑖, and 𝐸 (𝑝𝑖) is approximate energy consumed by 𝑝𝑖.

Step 2: The energy consumption, number of

hops and delay represent design variables.

According to those ones a random population is

generated, where the number of paths corresponds to

the number of learners. This population is expressed

as:

𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 = (

𝑥1,1 𝑥1,2 𝑥1,3⋮ ⋱ ⋮𝑥𝑚,1 𝑥𝑚,2 𝑥𝑚,3

)

The respective objective values are:

(

𝑓1⋮𝑓𝑖⋮𝑓𝑚)

Step 3 (Teacher phase): The mean of each

population column:

𝑀𝑒𝑎𝑛 = (𝑚1 𝑚2 𝑚3)

Teacher is the best solution for this iteration:

𝑋𝑡𝑒𝑎𝑐ℎ𝑒𝑟 = 𝑚𝑖𝑛 1≤𝑖≤𝑚(𝑓(𝑋𝑖)),

𝑋𝑖 = [𝑥𝑖,1, 𝑥𝑖,2 , 𝑥𝑖,3]

The difference between both values is expressed as:

𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝐷 = 𝑟(𝑋𝑡𝑒𝑎𝑐ℎ𝑒𝑟 − 𝑇𝐹𝑀𝑒𝑎𝑛)

According to this difference a new solution is

calculated 𝑋𝑛𝑒𝑤 . 𝑋𝑛𝑒𝑤 is accepted if its objective

value is better than 𝑋𝑜𝑙𝑑.

Figure.1 Initialization of population

Step 4 (Learner phase) Select a random 𝑋𝑗from

population, compare it to the current one 𝑋𝑖, then a

new solution is determined by using Eq. (3).

{𝑋𝑛𝑒𝑤, 𝑖 = 𝑋𝑜𝑙𝑑, 𝑖 + 𝑟𝑖(𝑋𝑖

– 𝑋𝑗), 𝑖𝑓 𝑓(𝑋𝑖) < 𝑓(𝑋𝑗)𝑋𝑛𝑒𝑤, 𝑖 = 𝑋𝑜𝑙𝑑, 𝑖 + 𝑟𝑖(𝑋𝑗

– 𝑋𝑖), 𝑖𝑓 𝑓(𝑋𝑗) < 𝑓(𝑋𝑖)

(3)

Step 5: If the number of generations is achieved

stop process, otherwise go to step 3.

3.3 Illustrative example

In this example, 10 nodes are deployed

randomly. The source node s sends request packets

to neighbors and then initializes the population (see

Fig. 1).

Nodes 1, 2 and 4 receive a request from the

source node. Each one checks the routing table, and

then requests their neighbors and so on until

reaching the sink. After this procedure, a population

of 5 paths is generated. (See Fig. 1)

Population:

Initial population composed by:

𝑝1 = {𝑆, 1, 2, 3, 𝑆𝑖𝑛𝑘} 𝑝2 = {𝑆, 2, 3, 𝑆𝑖𝑛𝑘}

𝑝3 = {𝑆, 4, 5, 𝑆𝑖𝑛𝑘} 𝑝4 = {𝑆, 2, 4, 9, 5, 𝑆𝑖𝑛𝑘} 𝑝5 = {𝑆, 4, 2, 3, 𝑆𝑖𝑛𝑘}

Design Variables:

The considered design variables are:

Consumed Energy: 𝐸(𝑝𝑖), 0 < 𝑖 < 6

Delay: 𝑑 (𝑝𝑖)

Number of hops in 𝑝𝑖

For 𝑝1:

𝐸(𝑝1) = 0.0058



𝑑(𝑝1) = 2.9645

Number of hops in 𝑝1 is 5

After the same calculation for all paths 𝑝𝑖 , the

population is:

𝑃𝑜𝑝 =

(

0.0058 2.9645 50.00460.00470.0071

2.37162.37163.5574

446

0.0060 2.9645 5)

TLBO implementation:

Objective function:

𝑓 =

(

0.00340.00270.00280.00420.0036)

Iteration 1:

{Teacher Phase}

𝑀𝑖𝑛(𝑓) = 0.0027 𝑃𝑜𝑝(2) = [0.0046 2.3716 4]

𝑝2 = {𝑆, 2,3, 𝑆𝑖𝑛𝑘} 𝑋𝑇𝑒𝑎𝑐ℎ𝑒𝑟 = [0.0046 2.3716 4] 𝑀𝑒𝑎𝑛 = (0.0056 2.8459 5.000) 𝑇𝐹 = 1 and 𝑟 = 0.1968

𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝐷 = [−0.0007 − 0.3216 − 0.6781]

𝑋𝑛𝑒𝑤,1 = [0.0051 2.6429 5.0000 ] 𝑓(𝑋𝑛𝑒𝑤,1) = 0.0027

𝑓(𝑋𝑜𝑙𝑑,1) = 0.0034 𝑓(𝑋𝑛𝑒𝑤,1) < 𝑓(𝑋𝑜𝑙𝑑,1)

𝑋1 = 𝑋𝑛𝑒𝑤,1 {Learner Phase}

Selecting a random solution 𝑋𝑗 (𝑖 ≠ 𝑗) 𝑗 = 2

𝑋1 = [0.0051 2.6429 5.0000]

𝑋2 = [0.0046 2.3716 4.0000]

𝑓 (𝑋1) = 0.0027

𝑓(𝑋2) = 0.0027

𝑟 = 0.0246

𝑋𝑛𝑒𝑤,1 = 𝑋1 − 𝑟(𝑋2 – 𝑋1) 𝑋𝑛𝑒𝑤,1 = [ 0.0053 2.7404 5.0000]

𝑃𝑜𝑝(1) = 𝑋𝑛𝑒𝑤,1

𝑃𝑜𝑝 =

(

0.0053 2.7404 5.00000.00460.00470.0071

2.37162.37163.5574

4.00004.00006.0000

0.0060 2.9645 5.0000)

Iteration 2:

{Teacher Phase}

𝑀𝑒𝑎𝑛 = ( 0.0055 2.8011 5.0000 )

𝑇𝐹 = 2 and 𝑟 = 0.6799

𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝐷 = [−0.0044 − 2.1965 − 4.0794]

𝑋𝑛𝑒𝑤,2 = [0.0002 0.1751 4.0000 ]

𝑓(𝑋𝑛𝑒𝑤,2) = 8.8305𝑒−06

𝑓(𝑋𝑜𝑙𝑑,2) = 0.0027

𝑓(𝑋𝑛𝑒𝑤,2) < 𝑓(𝑋𝑜𝑙𝑑,2)

𝑋2 = 𝑋𝑛𝑒𝑤,2 {Learner Phase}

Selecting a random solution 𝑋𝑗 (𝑖 ≠ 𝑗) 𝑗 = 3

𝑋2 = [ 0.0002 0.1751 4.0000]

𝑋3 = [ 0.0047 2.3716 4.0000]

𝑓(𝑋2) = 8.8305𝑒−06

𝑓(𝑋3) = 0.028

𝑓(𝑋3) > 𝑓(𝑋2) 𝑟 = 0.4299 𝑋𝑛𝑒𝑤,2 = 𝑋2 − 𝑟(𝑋3 – 𝑋2) 𝑋𝑛𝑒𝑤,2 = [−0.0017 − 0.7691 4.0000 ]

𝑃𝑜𝑝(2) = 𝑋𝑛𝑒𝑤,2

𝑃𝑜𝑝 =

(

0.0053 2.7404 5.0000−0.00170.00470.0071

−0.76912.37163.5574

4.00004.00006.0000

0.0060 2.9645 5.0000)

…

After a number of generations:

𝑃𝑜𝑝 =

(

0.0006 0.3834 5.00000.0007−0.0004−0.0007

0.7529−0.0997−0.3933

4.00004.00006.0000

0.0013 0.8347 5.0000)

𝑓 = 1.0𝑒−03 ∗

(

0.04920.12470.01000.04910.2204)

𝑀𝑖𝑛(𝑓) = 0.0100 ∗ 1.0𝑒−03 Best solution is:

𝑃𝑜𝑝(3) = (0.0065 3.2869 5.5438 ) 𝑝3 = {𝑆, 4,5, 𝑆𝑖𝑛𝑘}

Data transmission:

Source node splits data to 𝑁 parts and sent packets

to the sink through the chosen path 𝑝3 as described

in Fig. 2.

If a node of 𝑝3 fails, the previous node in the path

must search a new path, using the same process as

source node.



Figure.2 Data transmission

Figure.3 Warning packets

• Warning Packet:

The sink combines received parts to find

information about detected event. If one or many

parts missing, sink sends a warning packets toward

the source node 𝑠 (See Fig. 3) then s sends back the

missing parts.

4. Experimentations and results

This section evaluates the performance of the

proposed routing algorithm based on the TLBO. We

adopt the same conditions to have good

visualization capabilities for the experimental and

comparison purpose.

4.1 Experimental data

We perform simulations in MATLAB under 64

bits Windows operating system. Experiments are

conducted on a computer with Intel(R) i5 3:20GHz

CPU, and 4GB of RAM. The parameter settings

used in the simulations are shown in Table 1.

The radio energy dissipation model used is

“First Order Radio Model” of Heinzelman et al. [21]

mentioned in Fig. 4.

Table 1. Simulations parameters

Parameter Value

No. of nodes 10, 20, 30, 40, ..., 90,100

Simulation area 200m2, 300m2, … 1000m2

Topology Flat topology

Network type Static network

Deployment type Random deployment

Propagation model Free space radio propagation model

Energy model First Order Radio Model [21]

Initial node energy 10 Joules

No. of simulations 10 runs

Figure 4: First order radio model [21].

The main parameters of the wireless sensor

network model are: the initial energy for each node

is 10𝐽 , energy of electronic transmission 𝐸𝑒𝑙𝑒𝑐 = 50𝑛𝐽/𝑏𝑖𝑡 and amplification 휀𝑎𝑚𝑝 = 100𝑛𝐽/𝑏𝑖𝑡/𝑚2.

In order to monitor a static phenomenon, we

deploy randomly a number of nodes varied

according to the coverage area. A source node

detects an event then, using the routing approaches

transmits the information to the sink. The sink

location is known for the source node (if the sink is

not in the coverage area of the source node).

The experiments performed to evaluate the

TLBO approach proposed and to illustrate its

performance against the ACO approaches [8, 15].

The following section shows the results found.

4.2 Obtained results and comparison

In this section, we present simulation results that

evaluate the proposed approach based on TLBO and

validate its ability of well approximating the energy

consumption in the WSN. In addition two well-

known ACO based WSN routing algorithms, were

used to make comparisons; EEABR and IACO.

The Fig. 5 shows the results found for the

routing approaches ACO, EEABR and TLBO. The

average residual energy increases relatively to the

variation of number of the packets received. It is



(a)

(b)

(c)

(d)

(e)

(f)



(g)

(h)

(i)

Figure.5 Simulation results for different WSNs: (a) 20

nodes, (b) 30 nodes, (c) 40 nodes, (d) 50 nodes, (e) 60

nodes, (f) 70 nodes, (g) 80 nodes, (h) 90 nodes, and (i)

100 nodes

Figure.6 Average residual energy after 256 packets are

received

evident to conclude from the same figure that the

TLBO approach results exceed greatly the IACO

and the EEABR, even when the number of nodes

changes. The strategy of TLBO used to choose the

optimal path, lets the data reach the sink faster by

low energy consumption. Overall, comparing TLBO

and ACO metaheuristics for routing, the TLBO

optimization promises better results.

Fig. 6 shows the average residual energy after

256 packets received by the sink node for different

WSNs having various numbers of nodes. From the

Fig .6, it is seen that the difference in energy levels

increases as the number of nodes in the network

grows.

TLBO algorithm has a basic yet important

advantage, which is the absence of parameters. Thus,

its simplicity makes the adaptation for NP-hard

problems easier than other metaheuristics. Ant

colony optimization is one of the metaheuristics

used for routing problems in WSN and gave good

results. However, the number of parameters in ACO

makes TLBO better suited for routing in WSNs as

illustrate in the Fig. 6.

4.3 Discussions

TLBO is a population-based optimization

technique, which implements a group of solutions to

seek for the optimum solution. Many optimization

methods require algorithm parameters like

population size, number of generations, elite size,

etc. In addition to the common control parameters,

different algorithms require their own algorithm

specific control parameters that affect the

performance of the algorithm. IACO and EEABR

require exponent parameters, pheromone

evaporation rate and reward factor. Unlike other



optimization techniques, TLBO approach proposed

does not require any algorithm parameters to be

tuned, thus making the implementation of TLBO

simpler. TLBO is an algorithm-specific parameter-

less algorithm that uses the best solution of the

iteration to change the current solution in the

population thereby increasing the convergence rate.

TLBO does not divide the population, it uses two

different phases: ‘Teacher Phase’ and ‘Learner

Phase’ and uses the mean value of the population to

update the solution.

TLBO algorithm also has not any special

mechanism to handle the constraints. Considering

this fact, the proposed TLBO approach for routing

in wireless sensor networks is better than the IACO

and the EEABR regarding the algorithms

implementation and adaptation to the routing

problem. In addition as illustrated by the results

found in the Fig. 5 and 6, the TLBO approach

optimize the energy in different WSNs better than

the other approaches.

5. Conclusion

This paper presents a new optimization approach

based on a metaheuristic inspired by teacher and

learners behaviours (TLBO). Teaching learning

based optimization is a recent metaheuristic

proposed for continuous optimization problems.

TLBO is an algorithm-specific parameter-less

algorithm considering this fact, TLBO is efficiently

adapted to the routing in WSNs; despite it is a

discrete optimization problem. The proposed

approach provides good results in terms of energy

consumption and WSN’s lifetime. Various

experimentations were performed using the same

settings, in order to validate the proposal. We

demonstrate that the TLBO approach overcomes

widely the ACO based approaches: IACO and

EEABR, especially in energy consumption, which is

a very crucial factor in the wireless sensors networks.

In the near future, we engage to implement our

algorithm in a real wireless sensor network

environment in order to further investigate the

efficiency of our method.

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